### MIT scientists create doomsday AI that will take over if it detects an asteroid heading toward Earth

A team of scientists at MIT have developed a computer program that will help humans decide how to best deal with the end of the world, so long as that comes in form of a catastrophic asteroid collision. Experts say there as many as two or three new asteroids, sometimes called'Near Earth Objects,' discovered every night. It's inevitable that one of these asteroids will eventually end drifting into a collision course with Earth. 'People have mostly considered strategies of last-minute deflection, when the asteroid has already passed through a keyhole and is heading toward a collision with Earth," Sung Wook Paek, of MIT's Department of Aeronautics and Astronautics, told MIT News. Paek's team designed the program to evaluate the mass, momentum, trajectory, and time before projected impact to aid humans with the high-stakes decision making involved in averting global catastrophe.

### Testing for a causal effect (with 2 time series)

"… an old variable explains 85% of the change in a new variable. So we can talk about causality" Nevertheless, that was frustrating, and I was wondering if there was a clever way to test for causality in that case. A popular one is Granger causality (I can mention a paper we published a few years ago where we use such a test, Tents, Tweets, and Events: The Interplay Between Ongoing Protests and Social Media). With off-diagonal terms of matrix \Omega, we have a so-called instantaneous causality, and since \Omega is symmetry, we will write x\leftrightarrow y. With off-diagonal terms of matrix \boldsymbol{A}, we have a so-called lagged causality, with either \textcolor{blue}{x\rightarrow y} or \textcolor{red}{x\leftarrow y} (and possibly both, if both terms are significant).

### Product Price Prediction: A Tidy Hyperparameter Tuning and Cross Validation Tutorial

This is a machine learning tutorial where we model auto prices (MSRP) and estimate depreciation curves.

### Artificial intelligence yields new antibiotic

Using a machine-learning algorithm, MIT researchers have identified a powerful new antibiotic compound. In laboratory tests, the drug killed many of the world's most problematic disease-causing bacteria, including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models. The computer model, which can screen more than a hundred million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs. "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery," says James Collins, the Termeer Professor of Medical Engineering and Science in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering.

### Facebook will pay for users' voice recordings after it was caught listening to Messenger chats

Facebook says it will start paying users to harvest their voice data for training speech recognition software after it was caught analyzing their speech without permission last year. In a program called'Pronunciations', participants will be payed a small sum, only up to \$5, to use the company's market research app Viewpoints for recording various words and phrases that the company will then leverage to train its speech recognition AI. That voice data will be used to improve products like Portal, which is Facebook's smart display that can be used for video-calling among other things and can be activated with one's voice. In the program, participants, who must be at least 18-years-old, will have to utter specific phrases like'Hey Portal' and also say the first names of 10 of their friends on Facebook. For each'set' of prompts participants will receive 200 points.

### AI Is Used to Discover a Novel Antibiotic

Researchers announced the breakthrough discovery of a new type of antibiotic compound that is capable of killing many types of harmful bacteria, including deadly antibiotic-resistant strains, and published their findings in Cell on February 20. What makes this remarkable is that the researchers, from the Massachusetts Institute of Technology (MIT), Harvard, and McMaster University, used machine learning (a form of artificial intelligence) to discover the new antibiotic--an achievement that heralds the disruption of traditional research and drug development processes deployed by pharmaceutical industry behemoths. Antibiotic resistance is a global threat that is exacerbated by the overuse of antibiotics in livestock, the proliferation of antimicrobials in consumer products, and over-prescription in health care. Though estimating the future impact is challenging, one report predicted that by 2050, 10 million deaths per year could result from antimicrobial-resistant (AMR) infections. Combating the problem of antimicrobial resistance requires bringing novel compounds to market.

### OpenText Analyst Summit 2020 day 2: Digital Accelerants

Although technically a product breakout, the session on OpenText's Digital Accelerants product collection was presented to the entire audience as our last full-audience session before the afternoon breakouts. This was split into three sections: cloud, AI and analytics, and process automation. Jon Schupp, VP of Cloud GTM, spoke about how information is transforming the world: not just cloud, but a number of other technologies, a changing workforce, growing customer expectations and privacy concerns. Cloud, however, is the destination for innovation. Moving to cloud allows enterprise customers to take advantage of the latest product features, guaranteed availability, global reach and scalability while reducing their operational IT footprint.

### NASA will try and use InSight Lander's robotic arm to 'push' a troubled probe back into position

NASA is running out of options in its mission to get its InSight lander's probe back on track. According to the agency, it will attempt to use a robotic arm attached to its InSight Lander to push down on a probe meant to drill into Martian soil which has struggled to achieve its mission throughout the past year. NASA says the goal is to stop the probe from popping out of its partially dug hole which it has done twice in recent months in addition to almost burying itself. While the act of pushing down on the probe with the arm should be relatively easy, NASA acknowledges that choosing to do so could create problems for the instrument if too much force is applied. The worry is that pushing down with the arm may damage a ribbon-like stretch of wires that attaches to InSight.

### Microsoft Injects New AI Features Into Dynamics 365

Microsoft on Wednesday unveiled several new artificial intelligence capabilities across Dynamics 365 applications and a new solution to help project-centric services organizations transform their operations. The AI enhancements include first- and third-party data connections in Dynamics 365 Customer Insights, Microsoft's customer data platform (CDP). "The work in AI and CDP is new and a key part of Microsoft taking their products to an AI-driven approach," noted Ray Wang, principal analyst at Constellation Research. The company also unveiled new manual and predictive forecasting capabilities for Dynamics 365 Sales and Dynamic 365 Sales Insights. "Integration with the CDP is important, but more important will be the ability to automate transactions and apply AI to drive the next best action," Wang told CRM Buyer.

### Global Big Data Conference

When Google Flu Trends was launched in 2009, Google's chief economist, Hal Varian, explained that search trends could be used to "predict the present." At the time, the notion that useful patterns and insights could be extracted from large-scale search query data made perfect sense. After all, many users' digital journeys begin with a search query -- including 8 out of 10 people seeking health-related information. So what could possibly go wrong? The answer is infamous in the business and data science communities.